Optimally pruned extreme learning machine with ensemble of regularization techniques and negative correlation penalty applied to automotive engine coldstart hydrocarbon emission identification

In this investigation, the authors test the efficacy and reliability of optimally pruned extreme learning machine with ensemble of regularization techniques to identify the exhaust gas temperature (T"e"x"h) and the engine-out hydrocarbon emission (HC"r"a"w) during the coldstart operation of automotive engines. These variables have significant impact on the cumulative tailpipe emissions (HC"c"u"m) during a coldstart phenomenon, which is the number one emission-related problem for today's spark-ignited (SI) engine vehicles. To do so, the concepts of ensemble computing with negative correlation learning (NCL) and pruning of neurons are used in tandem to cope with difficulties associated with extracting knowledge from collected database. In the proposed framework, the regularization strategies are adopted to help us increasing the numerical stability of identifier while mitigating the redundant complexity of hidden neurons. Moreover, to increase the generalization of identifier and also reduce the effects of uncertainty, an ensemble of independent OP-ELM with NCL selection criterion called OP-ELM-ER-NCL is taken into account. To endorse the valid performance of OP-ELM-ER-NCL for modeling the characteristics of engine over the coldstart phenomenon, its performance is compared to a set of well-known identification systems, i.e. standard extreme learning machine (ELM), back-propagation neural network (BPNN), OP-ELM with different types of regularization, ensemble of regularized OP-ELM without negative correlation (OP-ELM-ER), and an ensemble ELM with a constrained linear system of leave-one-out outputs (E-LL), in terms of both accuracy and computational complexity. The simulation results indicate that the proposed identifier is really capable of capturing the knowledge of collected database. It is observed that its resulted accuracy and robustness are comparable with those obtained by identification methods available in the literature. Besides, using NCL strategy aids the ensemble to select the most effective regularization techniques and remove the redundant (ineffective) ones, which consequently decreases the complexity of final ensemble.

[1]  David M. Allen,et al.  The Relationship Between Variable Selection and Data Agumentation and a Method for Prediction , 1974 .

[2]  Lifeng Xi,et al.  Evolving artificial neural networks using an improved PSO and DPSO , 2008, Neurocomputing.

[3]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[4]  Ilya V. Kolmanovsky,et al.  Recurrent neural network training for energy management of a mild Hybrid Electric Vehicle with an ultra-capacitor , 2009, 2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems.

[5]  Thomas W. Asmus,et al.  Cycle-by-Cycle Analysis of HC Emissions During Cold Start of Gasoline Engines , 1995 .

[6]  Michel Verleysen,et al.  Ensemble Modeling with a Constrained Linear System of Leave-One-Out Outputs , 2010, ESANN.

[7]  Timo Similä,et al.  Multiresponse Sparse Regression with Application to Multidimensional Scaling , 2005, ICANN.

[8]  José David Martín-Guerrero,et al.  Regularized extreme learning machine for regression problems , 2011, Neurocomputing.

[9]  Emrah Tolga Yildiz Nonlinear constrained component optimization of a plug-in hybrid electric vehicle , 2010 .

[10]  Jose Carlos,et al.  Engine modeling and control for minimization of hydrocarbon coldstart emissions in SI engine , 2007 .

[11]  Nasser L. Azad,et al.  Determining Model Accuracy Requirements for Automotive Engine Coldstart Hydrocarbon Emissions Control , 2012 .

[12]  Amaury Lendasse,et al.  Extreme Learning Machine: A Robust Modeling Technique? Yes! , 2013, IWANN.

[13]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[14]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[15]  J. K. Hedrick,et al.  Automotive engine hybrid modelling and control for reduction of hydrocarbon emissions , 2006 .

[16]  Pannag R. Sanketi,et al.  Coldstart modeling and optimal control design for automotive SI engines , 2009 .

[17]  Lei Chen,et al.  Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.

[18]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[19]  Amaury Lendasse,et al.  TROP-ELM: A double-regularized ELM using LARS and Tikhonov regularization , 2011, Neurocomputing.

[20]  Xin Yao,et al.  Selective negative correlation learning approach to incremental learning , 2009, Neurocomputing.

[21]  P. N. Suganthan,et al.  Ensemble of niching algorithms , 2010, Inf. Sci..

[22]  Ahmad Mozaffari,et al.  Identifying the behaviour of laser solid freeform fabrication system using aggregated neural network and the great salmon run optimisation algorithm , 2012, Int. J. Bio Inspired Comput..

[23]  Xin Yao,et al.  Evolutionary ensembles with negative correlation learning , 2000, IEEE Trans. Evol. Comput..

[24]  Gérard Bloch,et al.  Incorporating prior knowledge in support vector machines for classification: A review , 2008, Neurocomputing.

[25]  Lotfi A. Zadeh,et al.  Is there a need for fuzzy logic? , 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.

[26]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[27]  Qinghua Zheng,et al.  Regularized Extreme Learning Machine , 2009, 2009 IEEE Symposium on Computational Intelligence and Data Mining.

[28]  Jonathan Timmis,et al.  Application areas of AIS: The past, the present and the future , 2008, Appl. Soft Comput..

[29]  Amaury Lendasse,et al.  OP-ELM: Optimally Pruned Extreme Learning Machine , 2010, IEEE Transactions on Neural Networks.

[30]  Ahmad Mozaffari,et al.  Modeling a shape memory alloy actuator using an evolvable recursive black-box and hybrid heuristic algorithms inspired based on the annual migration of salmons in nature , 2014, Appl. Soft Comput..

[31]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..